Mixture estimation with state-space components and Markov model of switching

Nagy, Iván; Suzdaleva, Evgenia

doi:10.1016/j.apm.2013.05.038

Cited by 11 publications

(10 citation statements)

References 12 publications

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“…The approach also contributes to the systematic extension of the recursive mixture estimation algorithms published in [29,30]. However, the open problems still remain, including e.g., the following:…”

Section: Discussionmentioning

confidence: 99%

“…to be normalized via (28), (29) where the proximity L i;xt is obtained according to (26) with the previous point estimatesθ i;t−1 andR i;t−1 . In (29) the point estimateθ i;t−1 ,R i;t−1 andα i;t−1 are obtained at the previous time instant t − 1 either using the direct updates (20), (21) and (22) respectively (if the value of y t is observed), or with the help of the mixture estimation algorithm from [18] as follows.…”

Section: On-line Multinomial Mixture-based Logistic Regressionmentioning

confidence: 99%

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On-Line Mixture-Based Alternative to Logistic Regression

Nagy¹,

Suzdaleva²

2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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Section: Discussionmentioning

confidence: 99%

Section: On-line Multinomial Mixture-based Logistic Regressionmentioning

confidence: 99%

On-Line Mixture-Based Alternative to Logistic Regression

Nagy¹,

Suzdaleva²

2016

NNW

Self Cite

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“…In this study, contrary to the difference-in-differences method [35,36] commonly used in this type of research, novel Bayesian methods are used. In particular, the innovative applications of Bayesian structural time series and Bayesian dynamic mixture models are implemented [37,38].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, despite the many advantages, Bayesian methods are characterized by high computational complexity and the necessity to approximate distributions [39]. Fortunately, in the case of the particular methods mentioned herein, the approximations are only at the numerical level of the parameters-not the functional forms of the distributions themselves [38], which is an important advantage in itself, and the computation complexity is at a very acceptable level [37].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data

Drachal

Cortés

2022

IJERPH

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Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.

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Reliability for discrete state systems with cyclic missions periods

Cui

Shen

et al. 2016

Applied Mathematical Modelling

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Mixture estimation with state-space components and Markov model of switching

Cited by 11 publications

References 12 publications

On-Line Mixture-Based Alternative to Logistic Regression

On-Line Mixture-Based Alternative to Logistic Regression

Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data

Reliability for discrete state systems with cyclic missions periods

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